Adaptive application of metal artifact correction algorithms

ABSTRACT

An apparatus for and a method of correcting an image for an image artifact. An initial image is corrected by an image artifact corrector ( 190 ). The so corrected sample correction image is compared with the initial image to obtain information on the corrective action. The corrective action is then adaptively reapplied by a controller ( 140 ) to obtain an improved corrected image thereby ensuring previously present artifacts are removed and creation of new artifacts are avoided.

FIELD OF THE INVENTION

The invention relates to an apparatus for correcting an image for animage artifact, to a method of correcting an image for an imageartifact, to a medical image processing system for correcting an imagefor an image artifact, to a computer program product, and to a computerreadable medium.

BACKGROUND OF THE INVENTION

Metal implants or other high density parts often cause image artifactsin CT images. Metal artifact reduction (MAR) algorithms are know whichreduce the image artifacts in most cases. An MAR algorithm is known fromU.S. Pat. No. 7,991,243.

However, there is the remaining problem that even the best algorithmsknown so far occasionally create new artifacts.

SUMMARY OF THE INVENTION

There is therefore a need to improve image artifact reductionalgorithms.

The object of the present invention is solved by the subject matters ofthe independent claims, wherein further embodiments are incorporated inthe dependent claims.

It should be noted that the aspects described in the following applyalso to the method of correcting an image for an image artifact, to themedical image processing system for correcting an image for an imageartifact, to the computer program product, and to a computer readablemedium.

According to one aspect there is provided an apparatus for correcting animage for an artifact.

The apparatus comprises an input unit for receiving an initial image, aprocessing unit for processing the image to effect the correction and anoutput unit for outputting a corrected version of the initial image.

The processing unit comprises a correction sampler configured to forwardthe initial image to an artifact corrector and to receive therefrom asample of a corrected image. The sample image is the result of acorrective action applied to the initial image by the artifactcorrector.

The processing unit further comprises a comparator configured to comparethe initial image and the corrected sample image to so establish acorrector image representing the corrective action of the artifactcorrector on the initial image.

The processing unit further comprises an artifact correction controllerconfigured to adaptively re-apply the respective corrective action in aweighted manner to any one of a plurality of image points in the initialimage. The weights or weight factor used per image point for theweighting of the corrective action are related to a degree to whichimage information in a neighbourhood around that image point iscompensable by corresponding image information in a correspondingneighbourhood in the corrector image,

The so adaptively re-corrected initial image is then output by theoutput unit as the corrected image.

In other words, the corrective action is adaptively re-applied by thecontroller to obtain an improved, final corrected image, therebyensuring previously present artifacts are removed and creation of newartifacts are avoided.

The apparatus allows applying an existing artifact correctorimplementing a known metal artifact reduction (MAR) algorithm.

The apparatus can be used as an “add-on” to existing MAR systems. Theapparatus is a “meta-image-artifact corrector” because it examinescorrection results output by existing MAR systems and then improves thecorrection results by adaptively re-applying the corrective action usingthe corrector image as a “roadmap”. The corrector image records theexisting MAR system's estimate of corrective action for the initialimage.

The apparatus acts in a “doubly” adaptive manner, namely in aquantitative and a spatial sense: controller determines where, that isat which image points in the initial image, the estimated correctiveaction is to be applied and how much, that is attenuated or amplified,of the corrective action is to be applied at that locale.

The apparatus operates in a two phase fashion: in a first, sample run,the artifact corrector is applied globally to the initial image toobtain the sample corrected image and to so gather information on thecorrective action of the algorithm on the initial image.

The corrective action is recorded in the corrector image. Using thecorrector image, the corrective action recorded therein is adaptivelyand selectively applied in a second, final run, but this time thecorrective action is applied only locally in image regions where the“correction image” shows features which are also present in theoriginal, initial image.

The controller acts neighbourhood-wise. Centred around each image pointin a previously defined region of the image plane (which may include thewhole of the image plane safe for a border area around the image frame),a sub-region or neighbourhood (“patch”) is defined. This patch is thenmapped into the corrector image to define a corrector imageneighbourhood corresponding to the initial image neighbourhood to soobtain for each image point a pair of neighbourhoods.

The compensatory degree (hereinafter the “degree”) measures thesuitability of the image information in the two neighbourhoods tocompensate or cancel each other. The degree or the corrective weightsassociated with the degree indicate the extent to which imageinformation is mirrored or is similar across the pairs ofneighbourhoods. As such the degree is a property of each of therespective pairs of initial image neighbourhoods and corrector imageneighbourhoods. Because the neighbourhoods correspond to each other,either of the neighbourhood can be said to “have” the degree.

High compensatory degree or capability of the neighbourhood results inmaintenance or even amplification of the corrective action (ideallynon-attenuated if artifact is completely compensated or annihilated,with weight or attenuation factor close to “1”) whereas low compensatorydegree or capability results in attenuation or complete elimination,with weight factor with absolute value less than 1 or even close tonaught.

In other words the higher the compensatory degree the higher theamplification of the corrective action at the respective centre imagepoint, and the lower the compensatory degree the higher the attenuationof the corrective action at the respective centre image point.

An image artifact is an image feature defined by a local pixel patternwhich is expected to show in the correction image also albeit inopposite pixel or voxel intensity so that the artifact can becompensated or annihilated after adaptive application of the correctorimage to the initial image.

All MAR algorithms can be formulated in a way that there is an initialimage, that suffers from metal artifacts and the algorithm creates acorrector image that is added to the initial image. The apparatus maytherefore be put to use for MAR algorithms do not follow this formexplicitly but they rather create directly a corrected image.

In one embodiment the difference between the corrected image and theoriginal image is calculated and this difference is used as thecorrector image.

The apparatus carries into effect the idea of applying the correctionimage “locally” in regions where it compensates artifacts but to avoidthe application where it would create new artifacts.

The apparatus allows distinguishing between these two cases by using theconcept of the compensatory degree. According to one embodiment, theweights are computed using an entropy measure for the combined imageinformation formed from the neighbourhood pair per centre initial imagepoint.

According to an alternative embodiment the statistical correlationcoefficient between the image information in each of the pairs ofneighbourhoods is computed.

According to one embodiment, the controller is configured to adjust apreviously set default size for the corrector image neighbourhood untilthe entropy of the image information in the corrector imageneighbourhood exceeds a predetermined threshold value.

Dynamically adjusting the neighbourhood size (measured in number ofpixels or voxels) allows to keep the run-time of the algorithm at bay asthis would increase with neighbourhood size. Choosing the thresholdentropy value allows to balancing size for run-time: the entropy costfunction may turn out rather flat for a too small a neighbourhood sizedue to lack of image information or structure in the smallneighbourhood. A too large a neighbourhood however is computationallyprohibitive and it further impedes a proper correction of artifactpresent in a small region.

The apparatus selects an appropriate neighbourhood size by accountingfor image structure in the neighbourhood, where “structure” is measuredby the entropy function, where the entropy of an image is preferablydefined as the entropy of the normalized histogram of grey values inthat neighbourhood. If the correction image is rather flat in aneighbourhood, then the pixel grey value histogram is highly peaked andhas high entropy. Using entropy as a structure measure, this means thata large neighbourhood should be selected. On the other hand, if thecorrection image has fine streaks in a neighbourhood, the histogramshows several peaks and has lower entropy. Consequently, a smallerneighbourhood can be selected.

It is understood that the apparatus may also be put to use with artifactcorrection algorithms other than MARs. The artifact may be caused notnecessarily by “metal” parts but by any other highly radiationattenuating part. The apparatus may be used for any image artifactcorrector whatever the cause for the image artifact or whatever theparticular algorithm underlying the existing artifact corrector.

In an alternative off-site embodiment the controller controls theproduction of the final corrected image at the remote artifactcontroller.

DEFINITIONS

Amplifying corrective action includes maintaining the corrective actionas provided by the MAR at a single given image point, the weightequalling at least unity in this later case.

Attenuation of corrective action includes eliminating the correctiveweight at a single given image point, the weight being around naught inthis case.

Image is to be construed broadly as an at least 2-dimensional array,matrix or similar data structure holding numerical data items, eachaddressable by at least two-dimensional coordinates i,j.

Image information or feature is a particular pixel value pattern givenby a particular pixel value distribution across the pixels making up thepatch or region in the image plane.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention will now be described withreference to the following drawings in which:

FIG. 1 schematically shows a block diagram of an apparatus for imagecorrection according to one embodiment of the invention;

FIG. 2 diagrammatically shows operation of the apparatus of FIG. 1according to one embodiment of the present invention;

FIG. 3 shows a flow chart of a method of correcting images according toone embodiment of the present invention

DETAILED DESCRIPTION OF EMBODIMENTS

To the right of FIG. 1 there is shown a block diagram of an apparatusfor correcting an image for an artifact.

The apparatus comprises an input unit or interface means 105, aprocessing unit 110 and an output unit or interface means 150.

Input 105 is configured to access a data base system 180 to retrievetherefrom an initial digital image having an image artifact.

Data base 180 may be arranged as a PACS in a medical facility and isholding medical image data such as 2D computed tomography (CT)projection images or reconstructed 2D or 3D cross sectional images, alsocommonly referred to as slice images or “slices”. The images are in asuitable digital format such as DICOM format. The apparatus is arrangedto connect via interface means 105 and a suitable computer communicationnetwork to data base 180.

To the left of FIG. 1 there is shown an artifact corrector module MAR190 implementing a known metal artifact reduction (MAR) algorithm. Theembodiment of the apparatus as diagrammatically shown in FIG. 1 isarranged as an “add-on” for an existing MAR 190 system. The MAR 190 mayhave access to additional data related to the image such as theprojection data on which the initial image is based on, or a prioriinformation about the highly attenuating part such as CAD information oforthopaedic implants.

In yet other embodiments however the apparatus may include a native MARas a component of processing unit 110.

The apparatus' processing unit 110 comprises a correction sampler 120, acomparator 130 and an artifact correction control 140.

In one embodiment, the apparatus components 105, 150, 120, 130 and 140are running as software routines on processing unit 110. However, adistributed architecture of the apparatus where the components areconnected in a suitable communication network is also contemplated inalternative embodiments. The components may also be arranged asdedicated FPGAs or hardwired standalone chips. The components may beprogrammed in a suitable scientific computing platform such as Matlab®or Simulink® and may then be translated into for, example, C++ or Croutines maintained in a library and linked when called on by processingunit 110.

Broadly speaking, the apparatus for correcting an image for an artifactconnects via input unit 105 to data base 180 and retrieves therefrom aninitial image having an image artifact. The image artifact may have beenthe result of a high density part such as a metal part residing in anobject whilst CT images are taken of the object.

The image may be a slice image reconstructed from a set of projectiondata acquired with a CT system.

The object of interest may be a part of a human or animal body and thehigh density parts may include a metallic part or other high densitypart such as a bone structure embedded in patient's soft tissue. Imageartifact such as streaks, distortions and shades may present in theacquired initial image due to the part exercising a higher attenuationon the radiation used in the CT run than the average attenuation of thesurrounding soft tissue.

After reading-in the initial image for image correction, correctionsampler 120 forwards initial image to MAR 190 and requests MAR 190 toprocess the initial image to output a MAR corrected sample image whichis then received back at the correction sampler 120.

The initial image and its MAR corrected sample image is then forwardedto a comparator 130. Comparator 130 compares the two images and producesa corrector image recording the corrective action of MAR 190 on theinitial image.

Corrector image represents the corrective action per image point ij ofthe MAR correction on the initial image. Corrector image is thenforwarded to artifact correction controller 140 to directly obtain thefinal corrected image by using the corrector image in combination withthe initial image.

Controller 140 then re-applies the corrective action to the initialimage as per corrector image but this time uses weights to modify thecorrective action per image point in the initial image. The individualweights or weight factors are computed by controller 140 as a parameter.Around each of the to-be corrected image points in the image plane ofinitial image, a patch or neighbourhood is defined by the controller.For each of the initial image neighbourhoods so defined a correspondingneighbourhood in the corrector image is defined by controller. For eachof the so defined pairs of neighbourhoods, controller 140 computes anumerical weight. Each weight measures the degree to which imageinformation in the initial neighbourhood is compensable by the imageinformation in the corrector image neighbourhood.

Controller 140 then looks up in the corrector image the correctiveaction that was previously estimated by the MAR 190 to be applied to anygiven point. The estimated corrective action is then weighted by therespective weight and the so re-weighted corrective action is thenapplied by controller 140 to the respective image point in the initialimage. Proceeding in this fashion for each or a selection of imagepoints in the initial image, the final corrected image is build-up. Theweights or parameters are computed by controller 140 according todifferent principles and each embodiment will be explained in moredetail below under the heading “Operation”.

Controller 140 then passes the final corrected image to output interfacemeans 150. The final corrected image can then be dispatched across thecommunication network to data base 180 or can be forwarded to an imagerenderer and rendered for view on a screen.

In another “off-site” embodiment, controller 140 instructs MAR unit tore-correct the initial image but this time the correction operation atMAR 190 is controlled by artifact correction controller 140 using thecorrector image as explained above. In this embodiment controller 140interfaces during runtime with MARs 190's algorithm and instructs theweights to be used in MAR 190's foreign algorithm. In this embodimentcontroller 140 is equipped with suitably programmed API's to effect theinteraction and the image correction algorithm at MAR 190 may have to besuitably adapted to carry into effect the control function of controller140 at MAR 190.

In the off-site embodiment, once the final corrected image is producedat MAR 190 under the control of controller 140, final corrected image isthen is then fed back to controller 140 which in turn passes theoff-site corrected image to output interface means 150. In yet anotherembodiment, MAR 190 may output the final corrected image and so releasesame for further post-processing to database 180 or otherwise.

Operation

Initial image μ_(ij) is formed as a matrix including rows i and columnsj. Matrix entry at row i and column j is a numerical value representinga grey value level of a pixel/voxel element. Each artifact in initialimage μ_(ij) is formed as distinctive image features defined by regionof those pixel and voxel elements. The following reference will be madeonly to pixel elements but it is understood that the following appliesequally to voxel elements.

The sample correction image σ_(ij) produced at MAR 190 and requested bycorrection sampler 120 is formed of the same number of rows and columnsas the image μ_(ij) but has in general different pixel value entriesbecause of the corrective action experienced at MAR 190.

Comparator 130 is configured to generate the corrector image c_(ij)based on initial image μ_(ij) and sample image σ_(ij). In one embodimentthe comparator 130 forms the pixel-wise difference between the initialimage μ_(ij) and sample image σ_(ij) resulting in the corrector imagec_(ij) having the same number of rows and columns as initial imageμ_(ij) or sample image σ_(ij). This difference value is thenrepresentative for the corrective action at that image point i,j.Corrector image c_(ij) along with initial image μ_(ij) is then forwardedfor processing to controller 140.

Controller 140 is configured to use corrector image c_(ij) and toadaptively apply a repeated correction to initial image μ_(ij).Controller 140 is configured to apply the correction image c_(ij) toinitial image μ_(ij) only locally, that is, application is restricted toregions where the corrective action of the corrector image c_(ij) wouldcompensate an artifact but application of corrected action is avoided orsufficiently attenuated in other regions of initial image μ_(ij) wherenew artifacts would be created.

The algorithm implemented by controller 140 allows distinguishingbetween those two cases which will be explained in more detail below.

FIG. 2 affords a diagrammatical explanation of the operation ofcontroller 140.

To the left of FIG. 2, initial image patch Ω_(mn) includes an artifactdiagrammatically shown as a dark ellipse. Corresponding corrector imagepatch Ω_(ij) includes matching or similar image structure or informationas shown in form of a light dashed ellipse matching the structure ininitial image patch Ω_(mn) for shape but shown with opposite greyvalues. So MAR 190, for dark ellipse artifact, correctly estimated thecorrective action because corrector image c_(ij) when applied to theimage points in patch Ω_(mn) would compensate dark ellipse artifactcompletely. Image information in patch Ω_(mn) has therefore a highcompensatory degree. Controller 140 therefore computes a parameter Alarger unity to at least maintain or even amplify the corrective actionas recorded by corrector image c_(ij) for image points in that pathpatch Ω_(mn).

Patch Ω_(kl) shows the opposite scenario. Here, MAR 190 incorrectlyestimated the corrective action because it would, if applied, introducea dark rectangular artifact (shown to the lower right in FIG. 3) wherethere is no artifact at all as shown in “clean” path Ω_(kl) to the lowerleft in FIG. 3. Controller 140 therefore computes for patch Ω_(kl) avery low compensatory degree, because the structures in both patches donot match. Control parameter A is therefore attenuative with a valueless than unity and close to naught to so annihilate or eliminate theMAR 190 proposed corrective action for image points in patch Ω_(kl).

According to one embodiment controller 140 reads in initial image μ_(ij)defines for each point i, j neighbourhood Ω_(ij) (also called a “patch”)around that point i,j. The point may be referred to as the centre pointof that patch, each patch having at least one such centre or “seed”point. Neighbourhood Ω_(ij) defines a sub set in the image plain and maybe given by a rectangular region with n×n pixel height and width. In anembodiment an 11×11 pixel square is chosen as the default size forneighbourhood Ω_(ij).

In other embodiments neighbourhood Ω_(ij) is a circle having a specifiedradius r around each of the image points i,j.

Controller 140 then maps this neighbourhood Ω_(ij) into a correspondingneighbourhood Ω_(ij) in corrector image c_(ij). According to oneembodiment this is done by using the same pixel co-ordinates as definedfor the corresponding neighbourhood in corrector image c_(ij).

In one embodiment, to save CPU time the neighbourhoods are not definedfor each and every pixel image pixel point in initial image μ_(ij) butare restricted to a region which is likely to include the artifact. Thisregion of interest in the image plane can be established for example byuse of a suitably programmed segmentor. The image points i, j as centrepoints for the respective neighbourhoods Ω_(ij) are chosen sufficientlyfar away from the border of image μ_(ij) to ensure that theneighbourhoods are well defined and would not extend beyond the currentframe.

In one embodiment controller 140 calculates the weights as thestatistical correlation coefficient between pixel values in the patchΩ_(ij) of initial image μ_(ij) and the corresponding patch Ω_(ij) incorrector image c_(ij). Calculation for statistical correlationcoefficient is according to the following formula:

$t_{ij} = \frac{\sum\limits_{{({i^{\prime},j^{\prime}})} \in \Omega_{ij}}{\left( {\mu_{i^{\prime}j^{\prime}} - \overset{\_}{\mu_{ij}}} \right)\left( {c_{i^{\prime}j^{\prime}} - \overset{\_}{c_{ij}}} \right)}}{\sqrt{\sum\limits_{{({i^{\prime},j^{\prime}})} \in \Omega_{ij}}{\left( {\mu_{i^{\prime}j^{\prime}} - \overset{\_}{\mu_{ij}}} \right)^{2}{\sum\limits_{{({i^{\prime},j^{\prime}})} \in \Omega_{ij}}\left( {c_{i^{\prime}j^{\prime}} - \overset{\_}{c_{ij}}} \right)^{2}}}}}$

where μ_(ij) and c_(ij) denote the average pixel value in initial imagepatch and corrector image patch, respectively.

The pixel values of the final corrected image f_(ij) are then calculatedaccording to:

f _(ij)=μ_(ij) −t _(ij) c _(ij)

In one embodiment, the range of values of t_(ij) are windowed andtransformed into a selectable range by a clipping-function A.

In one embodiment a partly sinusoidal function is defined:

${A\left( {t = t_{ij}} \right)} = \left\{ \begin{matrix}1 & {t < t_{0}} \\{0.5 + {0.5{\cos \left( {{\pi \left( {t + t_{0}} \right)}/t_{0}} \right)}}} & {t_{0} \leq t \leq 0} \\0 & {0 < t}\end{matrix} \right.$

In the formula A(t) designates control parameter which varies between 0for positive correlation (t_(ij)=1) and 1 for perfect anti-correlation(t_(ij)=−1), that is, negative correlation, and defines a smoothsinusoidal transition between these two extremes.

Cut-off parameter t₀ varies between minus 1 and zero and defines acut-off point at which the correlation coefficients t_(ij) areconsidered to be “negative enough” to warrant unchanged that isun-attenuated application of the corrective action by corrector imagec_(ij) at that point i,j.

Using the clipping function A(t), the final corrected image pixels arethen computed as:

f _(ij)=μ_(ij) +A(t)c _(ij)

In one embodiment correlation between the patches is made more robust bytracking with a segmentor, regions that are representative of bones orother high density matter. Those regions are then excluded from therespective patches prior to the calculation of the correlationcoefficients in respect of that patch.

In another embodiment controller 140 is configured to calculate theweights according to the entropy with respect to each pair of patches.

In the entropy based embodiment, the weight for each patch pair isdetermined by the following formula:

min_(A), H(μ_(Ω) _(ij) +A′c_(Ω) _(ij) )

In other words, controller 140 is configured to solve for:

A _(ij)=arg min H(Histogram(μ_(Ω) _(ij) +A′c _(Ω) _(ij) )); A′∈[a;b]⊂

  (1),

where H denotes the entropy function

${H(P)} = {- {\sum\limits_{i}{p_{i}\log \; p_{i}}}}$

and μ_(Ω) _(ij) and c_(Ω) _(IJ) denote initial image and correctionimage, each restricted to the selected patch Ω_(ij).

Entropy function H can be calculated by establishing a pixel levelintensity histogram for each patch as indicated in formula (1) above.Intervals (“bins”) of pixel values are defined for each patch and thenumber of pixels having a value falling in any one of the bins arerecorded.

Approximating for each bin the value p_(i) of P by the normalizedfraction of pixels having a value in the respective bin range yields thedesired histogram.

The histogram can then be used as an approximation of the probabilitymass function P=(p_(i)) of a random Variable X, each pixel value i,j inthe patch considered a sample of i.i.d. pixel value random variable X.

According to one embodiment, a=−0.5 and b=1.5 but it is understood thatanother reasonable solution space may also be chosen in accord with therequirements of the instant artifact correction problem. Allowing valueslarger than 1 accounts for a situation, where the artifact strength hasbeen underestimated. Negative values for A_(ij) may occur if the shapeof the artifact structure has been estimated correctly, but with inverseintensity.

In formula (1) for the corrective parameter, a linear combination of therestricted images is formed in the vector space of matrices. The scalarA_(ij) measures how much of the corrective action of corrector imagec_(ij) should added in patch Ω_(mn), thereby adding as little imageinformation as possible, that is, to minimize the entropy of thelinearly combined image.

In one embodiment the bins for the histogram are chosen at increments of10 Hounsfield Units (HU) and the default neighbourhood size is 11×11pixels.

The apparatus allows the user to configure bin levels and the defaultneighbourhood size.

Having solved for A at any given image point i,j, the corrective actionat the respective neighbourhood centre point i,j is then weighted byA_(ij) according to its degree as measured by the entropy of itsneighbourhood pair as the image structure measure:

f _(ij)=μij+A _(ij) c _(ij)

Using the entropy of the combined image of both patches as the weightensures that the resulting final corrected image has the minimum amountof image features. It is thereby ensured that application of thecorrector does not add new artifacts, that is, new image information orfeatures that were not previously in the initial image.

In one embodiment weights A_(ij) are restricted to the unit intervalwith a=0 and b=1. The parameter calculated as the minimum entropy in theunit interval will assume a value 1 or close to 1 if the corrector imagecontains only artifact structures that are already present in theinitial image μ_(ij).

Weight A_(ij) will assume a value of 0 or close to 0 if the correctorimage c_(ij) contains only new image information (a new artifact) thatis not present in the initial image μ_(ij).

In another embodiment the computed weight is reasonability checked in aBayesian estimator framework. Improbable values are discarded and resetto a most probable value, for instance by determining A_(ij) accordingto:

A _(ij)=min_(A) , H(μ_(Ω) _(ij) +A′c _(Ω) _(ij) )−p(A′),

where p is an a priori probability function (also called “a prior”) forthe weighting factor, for example a Gaussian distribution function withexpectation value of 1.

Either of the above embodiments corrector 140 can further be configuredto use the calculated entropy when selecting the size, for example, edgewidth or diameter of the patches. In this method the patch is selectedbased on the information content in that patch of the corrector imagec_(ij).

Choosing the patches in accordance with the entropy of the pixel regionsenclosed by the patch results in saving of CPU time and improvement ofthe quality of the finally corrected image. Using the entropy allowsdistinguishing image regions rich in information from flat regions wherethere is little structure. A flat region in correction image c_(ij) hasa pixel value histogram that is peaked and the region therefore has highentropy, in other words using the entropy function H as a structuremeasure will result in a larger than average neighbourhood or patch tobe selected for that region. On the other hand, if a region in thecorrection image c_(ij) contains more image structures such as streaks,shades or singularities, the pixel value histogram for that region islikely to show several peaks and consequently would have higher entropy.In this high entropy case controller 140 is configured to choose asmaller than average patch in that region. According to one embodimentpatches are therefore adaptively chosen according to the entropy of theregion enclosed by the patch.

According to one embodiment a default patch size is chosen and scaledaccording to the entropy calculated in that patch. Controller 140 uses aconfigurable average entropy value to affect a scaling with respect tothat average value.

In other embodiments, controller 140 implements other image structuremeasures than entropy such as variance of image values or mean absolutedifferences from the mean.

With reference to FIG. 3, a flow chart is shown of a method ofcorrecting an image for an image artifact.

In step S305, the initial image is received.

In step S310 the initial image is forwarded to an existing artifactcorrector to receive therefrom a sample of a corrected image. The samplecorrected image is the result of an estimated corrective action appliedto the initial image by the artifact corrector algorithm implemented bythe artifact corrector.

In step S320, the initial image and the corrected sample image arecompared. The result of the comparison is then recorded as a correctorimage representing the corrective action of the artifact corrector perinitial image point ij in the initial image.

In an optional step a default neighbourhood size is adjusted (S330)until the entropy of the image information in the previously set defaultsized corrector image neighbourhood exceeds a predetermined thresholdvalue. The threshold and the default size are both configurable and theadjustment means in general to enlarge the neighbourhood to secure anadequate amount of entropy. A reasonable amount of entropy may forexample be 0.05 or 0.1.

In step S340 the respective corrective action is then adaptivelyre-applied in a weighted manner to any one of a plurality of imagepoints in the initial image. The weights used per image point for theweighting of the corrective action relate to the degree to which imageinformation in a neighbourhood around that image point is compensable bycorresponding image information in a corresponding neighbourhood in thecorrector image. The plurality may include all image points in theinitial image (other than a band of image points along the border or theimage frame) or a selection of image points that has been established tomost likely include the image artifacts. A segmentation step may be usedto so suitable prune down the image plane to a region of interest.Optionally, in a similar manner, the image plane may be trimmed toexclude image background areas.

In one embodiment, the weights are computed to minimize perneighbourhood the entropy of a combination of image information fromboth, the respective initial image neighbourhood and the correspondingcorrector image neighbourhood.

In an alternative embodiment the weights are computed per neighbourhoodas a statistical correlation coefficient between image information inthe respective initial image neighbourhood and the corresponding imageinformation in the corresponding corrector image neighbourhood.

In another exemplary embodiment of the present invention, a computerprogram or a computer program element is provided that is characterizedby being adapted to execute the method steps of the method according toone of the preceding embodiments, on an appropriate system.

The computer program element might therefore be stored on a computerunit, which might also be part of an embodiment of the presentinvention. This computing unit may be adapted to perform or induce aperforming of the steps of the method described above. Moreover, it maybe adapted to operate the components of the above-described apparatus.The computing unit can be adapted to operate automatically and/or toexecute the orders of a user. A computer program may be loaded into aworking memory of a data processor. The data processor may thus beequipped to carry out the method of the invention.

This exemplary embodiment of the invention covers both, a computerprogram that right from the beginning uses the invention and a computerprogram that by means of an up-date turns an existing program into aprogram that uses the invention.

Further on, the computer program element might be able to provide allnecessary steps to fulfil the procedure of an exemplary embodiment ofthe method as described above.

According to a further exemplary embodiment of the present invention, acomputer readable medium, such as a CD-ROM, is presented wherein thecomputer readable medium has a computer program element stored on itwhich computer program element is described by the preceding section.

A computer program may be stored and/or distributed on a suitablemedium, such as an optical storage medium or a solid-state mediumsupplied together with or as part of other hardware, but may also bedistributed in other forms, such as via the internet or other wired orwireless telecommunication systems.

However, the computer program may also be presented over a network likethe World Wide Web and can be downloaded into the working memory of adata processor from such a network. According to a further exemplaryembodiment of the present invention, a medium for making a computerprogram element available for downloading is provided, which computerprogram element is arranged to perform a method according to one of thepreviously described embodiments of the invention.

It has to be noted that embodiments of the invention are described withreference to different subject matters. In particular, some embodimentsare described with reference to method type claims whereas otherembodiments are described with reference to the device type claims.However, a person skilled in the art will gather from the above and thefollowing description that, unless otherwise notified, in addition toany combination of features belonging to one type of subject matter alsoany combination between features relating to different subject mattersis considered to be disclosed with this application. For example, theembodiment is described with reference to a CT scanner but it can bereadily applied to 3D X-ray imaging as well. Furthermore, all featurescan be combined providing synergetic effects that are more than thesimple summation of the features.

While the invention has been illustrated and described in detail in thedrawings and foregoing description, such illustration and descriptionare to be considered illustrative or exemplary and not restrictive. Theinvention is not limited to the disclosed embodiments. Other variationsto the disclosed embodiments can be understood and effected by thoseskilled in the art in practicing a claimed invention, from a study ofthe drawings, the disclosure, and the dependent claims.

In the claims, the word “comprising” does not exclude other elements orsteps, and the indefinite article “a” or “an” does not exclude aplurality. A single processor or other unit may fulfil the functions ofseveral items re-cited in the claims. The mere fact that certainmeasures are re-cited in mutually different dependent claims does notindicate that a combination of these measures cannot be used toadvantage. Any reference signs in the claims should not be construed aslimiting the scope.

1. An apparatus for correcting an image for an artifact, the apparatuscomprising; an input unit for receiving an initial image; a processingunit or processing the image to effect the correction; an output unitfor outputting a corrected version of the initial image; the processingunit comprising: a correction sampler configured to forward the initialimage to an artifact corrector and to receive therefrom a sample of acorrected image, the sample image being the result of a correctiveaction applied to the initial image by the artifact corrector; acomparator configured to compare the initial image and the correctedsample image to so establish a corrector image representing thecorrective action of the artifact corrector on the initial image; anartifact correction controller configured to adaptively re-apply therespective corrective action in a weighted manner to any one of aplurality of image points in the initial image, the weights used perimage point for the weighting of the corrective action related to adegree to which image information in a neighbourhood around that imagepoint is compensable by corresponding image information in acorresponding neighbourhood in the corrector image, the output unitconfigured to output the so adaptively re-corrected initial image as thecorrected image.
 2. The apparatus of claim 1, wherein the weights areneighbourhood dependent and indicative of the compensatory degree, theweights attenuating or amplifying the corrective action at any givenimage point, the attenuation varying inversely to the compensatorydegree and the amplification varying directly with the compensatorydegree.
 3. The apparatus of claim 1, wherein the controller isconfigured to compute the weights to minimize per neighbourhood theentropy of a combination of image information from both, the respectiveinitial image neighbourhood and the corresponding corrector imageneighbourhood.
 4. The apparatus of claim 1, wherein the controller isconfigured to compute the weights per neighbourhood from a statisticalcorrelation coefficient between image information in the respectiveinitial image neighbourhood and the corresponding image information inthe corresponding corrector image neighbourhood.
 5. The apparatus ofclaim 1, wherein a size of the corresponding corrector imageneighbourhood is set by the controller to a default size, the controllerconfigured to adjust the size until the entropy of the image informationin the default corresponding corrector image neighbourhood exceeds apredetermined threshold value.
 6. A method of correcting an image for anartifact, the method comprising: receiving an initial image; forwardingthe initial image to an artifact corrector and to receive therefrom asample of a corrected image, the sample image being the result of acorrective action applied to the initial image by the artifactcorrector; comparing the initial image and the corrected sample image toso establish a corrector image representing the corrective action of theartifact corrector on the initial image; adaptively re-apply therespective corrective action in a weighted manner to any one of aplurality of image points in the initial image, the weights used perimage point for the weighting of the corrective action related to adegree to which image information in a neighbourhood around that imagepoint is compensable by corresponding image information in acorresponding neighbourhood in the corrector image.
 7. The method ofclaim 6, wherein the weights are neighbourhood dependent and indicativeof the compensatory degree, the weights attenuating or amplifying thecorrective action at any given image point, the attenuation varyinginversely to the compensatory degree and the amplification varyingdirectly with the compensatory degree.
 8. The method of claim 6, whereinthe step of adaptively re-applying the corrective action includescomputing the weights to minimize per neighbourhood the entropy of acombination of image information from both, the respective initial imageneighbourhood and the corresponding corrector image neighbourhood. 9.The method of claim 6, wherein the step of adaptively re-applying thecorrective action includes computing the weights per neighbourhood as astatistical correlation coefficient between image information in therespective initial image neighbourhood and the corresponding imageinformation in the corresponding corrector image neighbourhood.
 10. Themethod of claim 6, comprising: prior to the adaptively re-applying thecorrective action, adjusting a size until the entropy of the imageinformation in a previously set default sized corresponding correctorimage neighbourhood exceeds a predetermined threshold value.
 11. Amedical image processing system for correcting am image for an imageartifact, the system comprising: an apparatus of claim 1; the artifactcorrector; a database system holding the initial image.
 12. A computerprogram element for controlling an apparatus, which, when being executedby a processing unit is adapted to perform the method step of claim 7.13. A computer readable medium having stored thereon the program elementof claim 12.